This study, presented by Akay et. al., focused on developing an AI-based diagnostic model for Hirschsprung’s disease (HD) using deep learning on contrast enema (CE) images, with the goal of improving diagnostic accuracy while reducing the invasiveness of other procedures. The researchers employed Python, Pytorch, and a deep learning model based on the YOLOv8 algorithm. The model was trained and validated, emphasizing key metrics like mean average precision (mAP), precision, recall, and F1 score. A cohort of 725 contrast enema (CE) images from patients who underwent definitive surgery for HD from 2013 to 2022. Eligibility criteria required histopathological confirmation of HD, intraoperative full-thickness biopsies (FTBs), and perioperative contrast-enhanced scans. They used the Python programming language and the PyTorch algorithm for object detection. Two experienced pediatric surgeons, who regularly perform HD surgery, annotated the images. The model achieved strong results with a precision of 0.87477, a recall of 0.873717, and an mAP50 score of 0.91. The external validation regarding a different data set demonstrated a sensitivity of 86.96%, specificity of 72.22%, and overall accuracy of 80.49%. These findings indicated that the model has a high capability of identifying accurate HD cases while maintaining reliable accuracy in non-invasive image-based diagnosis. This AI model could serve as a useful screening tool to reduce the need for invasive biopsies. Researchers emphasized that while this sounds promising, the model should complement rather than replace the original, conventional diagnostic methods.